Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis

Diabetes Nutr Metab. 2002 Aug;15(4):215-21.


Diabetes is a major health problem in both industrial and developing countries, and its incidence is rising. Although detection of diabetes is improving, about half of the patients with Type 2 diabetes are undiagnosed and the delay from disease onset to diagnosis may exceed 10 yr. Thus, earlier detection of Type 2 diabetes and treatment of hyperglycaemia and related metabolic abnormalities is of vital importance. The objectives of the present study were to examine urine samples from Type 2 diabetic patients and healthy volunteers using the electronic nose technology and to evaluate possible application of data classification methods such as self-learning artificial neural networks (ANN) and logistic regression (LR) in comparison with principal components analysis (PCA). Urine samples from Type 2 diabetic patients and healthy controls were processed randomly using a simple 8-sensors electronic nose and individual electronic nose patterns were qualitatively classified using the "Approximation and Classification of Medical Data" (ACMD) network based on 2 output neurons, binary LR analysis and PCA. Distinct classes were found for Type 2 diabetic subjects and controls using PCA, which had a 96.0% successful classification percentage mean while qualitative ANN analysis and LR analysis had successful classification percentages of 92.0% and 88.0%, respectively. Therefore, the ACMD network is suitable for classifying medical and clinical data.

Publication types

  • Comparative Study

MeSH terms

  • Aged
  • Blood Glucose / analysis
  • Body Mass Index
  • Breath Tests
  • Diabetes Mellitus, Type 2 / diagnosis*
  • Diabetes Mellitus, Type 2 / urine*
  • Fasting
  • Female
  • Glycosuria
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Neural Networks, Computer*
  • Nose
  • Odorants / analysis*
  • Proteinuria / urine
  • Sensitivity and Specificity


  • Blood Glucose